About
We are interested in understanding how adaptive immune cells traffic into the brain and mediate pathological glial activation that inhibits myelin repair and causes progressive axonal degeneration in multiple sclerosis. The lab utilizes a combination of animal models, advanced imaging techniques, and in vitro work with human iPSCs to interrogate mechanisms of injury and develop strategies for neuroprotection and repair.
Research topics
- Computer Science
- Artificial Intelligence
- Medicine
- Internal medicine
- Pathology
- Data Mining
- Immunology
- Ophthalmology
- Natural Language Processing
- Biology
- Psychiatry
- Intensive care medicine
- Statistics
- Optometry
- Mathematics
- Oncology
- Anatomy
- Cancer research
- Neuroscience
- Algorithm
- Surgery
- Dermatology
- Genetics
Selected publications
Locus for severity implicates CNS resilience in progression of multiple sclerosis
Nature · 2023 · 218 citations
- Medicine
- Biology
- Genetics
2023
- Medicine
- Cancer research
- Internal medicine
<p>Brain and flank tumors are of equivalent mass. Brain and flank tumor mass was not different on day 17 when tissues were harvested for analysis. Experiment conducted x 2 with {greater than or equal to} 5 mice/group.</p>
From the prodromal stage of multiple sclerosis to disease prevention
Nature Reviews Neurology · 2022 · 81 citations
- Medicine
- Intensive care medicine
- Psychiatry
Multiple Sclerosis and Related Disorders · 2022 · 86 citations
- Medicine
- Ophthalmology
- Surgery
Journal of Neuro-Ophthalmology · 2022 · 19 citations
- Artificial Intelligence
- Computer Science
- Medicine
BACKGROUND: Spectral-domain (SD-) optical coherence tomography (OCT) can reliably measure axonal (peripapillary retinal nerve fiber layer [pRNFL]) and neuronal (macular ganglion cell + inner plexiform layer [GCIPL]) thinning in the retina. Measurements from 2 commonly used SD-OCT devices are often pooled together in multiple sclerosis (MS) studies and clinical trials despite software and segmentation algorithm differences; however, individual pRNFL and GCIPL thickness measurements are not interchangeable between devices. In some circumstances, such as in the absence of a consistent OCT segmentation algorithm across platforms, a conversion equation to transform measurements between devices may be useful to facilitate pooling of data. The availability of normative data for SD-OCT measurements is limited by the lack of a large representative world-wide sample across various ages and ethnicities. Larger international studies that evaluate the effects of age, sex, and race/ethnicity on SD-OCT measurements in healthy control participants are needed to provide normative values that reflect these demographic subgroups to provide comparisons to MS retinal degeneration. METHODS: Participants were part of an 11-site collaboration within the International Multiple Sclerosis Visual System (IMSVISUAL) consortium. SD-OCT was performed by a trained technician for healthy control subjects using Spectralis or Cirrus SD-OCT devices. Peripapillary pRNFL and GCIPL thicknesses were measured on one or both devices. Automated segmentation protocols, in conjunction with manual inspection and correction of lines delineating retinal layers, were used. A conversion equation was developed using structural equation modeling, accounting for clustering, with healthy control data from one site where participants were scanned on both devices on the same day. Normative values were evaluated, with the entire cohort, for pRNFL and GCIPL thicknesses for each decade of age, by sex, and across racial groups using generalized estimating equation (GEE) models, accounting for clustering and adjusting for within-patient, intereye correlations. Change-point analyses were performed to determine at what age pRNFL and GCIPL thicknesses exhibit accelerated rates of decline. RESULTS: The healthy control cohort (n = 546) was 54% male and had a wide distribution of ages, ranging from 18 to 87 years, with a mean (SD) age of 39.3 (14.6) years. Based on 346 control participants at a single site, the conversion equation for pRNFL was Cirrus = -5.0 + (1.0 × Spectralis global value). Based on 228 controls, the equation for GCIPL was Cirrus = -4.5 + (0.9 × Spectralis global value). Standard error was 0.02 for both equations. After the age of 40 years, there was a decline of -2.4 μm per decade in pRNFL thickness ( P < 0.001, GEE models adjusting for sex, race, and country) and -1.4 μm per decade in GCIPL thickness ( P < 0.001). There was a small difference in pRNFL thickness based on sex, with female participants having slightly higher thickness (2.6 μm, P = 0.003). There was no association between GCIPL thickness and sex. Likewise, there was no association between race/ethnicity and pRNFL or GCIPL thicknesses. CONCLUSIONS: A conversion factor may be required when using data that are derived between different SD-OCT platforms in clinical trials and observational studies; this is particularly true for smaller cross-sectional studies or when a consistent segmentation algorithm is not available. The above conversion equations can be used when pooling data from Spectralis and Cirrus SD-OCT devices for pRNFL and GCIPL thicknesses. A faster decline in retinal thickness may occur after the age of 40 years, even in the absence of significant differences across racial groups.
SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning
IEEE Transactions on Medical Imaging · 2020 · 182 citations
- Artificial Intelligence
- Computer Science
- Artificial Intelligence
High resolution magnetic resonance (MR) images are desired in many clinical and research applications. Acquiring such images with high signal-to-noise (SNR), however, can require a long scan duration, which is difficult for patient comfort, is more costly, and makes the images susceptible to motion artifacts. A very common practical compromise for both 2D and 3D MR imaging protocols is to acquire volumetric MR images with high in-plane resolution, but lower through-plane resolution. In addition to having poor resolution in one orientation, 2D MRI acquisitions will also have aliasing artifacts, which further degrade the appearance of these images. This paper presents an approach SMORE1 based on convolutional neural networks (CNNs) that restores image quality by improving resolution and reducing aliasing in MR images.2 This approach is self-supervised, which requires no external training data because the high-resolution and low-resolution data that are present in the image itself are used for training. For 3D MRI, the method consists of only one self-supervised super-resolution (SSR) deep CNN that is trained from the volumetric image data. For 2D MRI, there is a self-supervised anti-aliasing (SAA) deep CNN that precedes the SSR CNN, also trained from the volumetric image data. Both methods were evaluated on a broad collection of MR data, including filtered and downsampled images so that quantitative metrics could be computed and compared, and actual acquired low resolution images for which visual and sharpness measures could be computed and compared. The super-resolution method is shown to be visually and quantitatively superior to previously reported methods.
Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis
Scientific Reports · 2020 · 185 citations
- Computer Science
- Artificial Intelligence
- Computer Science
The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy which has proven useful. However, it offers diminishing insight when the number of objects is unknown, such as in white matter lesion segmentation of multiple sclerosis (MS) patients. We present a refinement for finer grained parsing of SDI results in situations where the number of objects is unknown. We explore these ideas with two case studies showing what can be learned from our two presented studies. Our first study explores an inter-rater comparison, showing that smaller lesions cannot be reliably identified. In our second case study, we demonstrate fusing multiple MS lesion segmentation algorithms based on the insights into the algorithms provided by our analysis to generate a segmentation that exhibits improved performance. This work demonstrates the wealth of information that can be learned from refined analysis of medical image segmentations.
Recent grants
NIH · $6.3M · 2020–2026
Imaging Neurodegeneration in Multiple Sclerosis
NIH · $436k · 2013–2018
NIH · $1.8M · 1989
NIH · $438k · 2014
Mechanisms by which effector T cells modulate endogenous remyelination
NIH · $5.8M · 2000–2026
Frequent coauthors
- 759 shared
Shiv Saidha
Johns Hopkins Medicine
- 614 shared
Laura J. Balcer
New York University
- 543 shared
Daniel S. Reich
National Institutes of Health
- 436 shared
Elliot M. Frohman
Stanford University
- 411 shared
Elias S. Sotirchos
Johns Hopkins Medicine
- 307 shared
Teresa C. Frohman
Stanford University
- 306 shared
Jiwon Oh
Kensington Health
- 265 shared
Jerry L. Prince
Johns Hopkins University
Awards & honors
- R01NS041435: “Mechanisms by which effector T cells modulate…
- R01NS082347: “Imaging Neurodegeneration in Multiple Sclerosi…
- U01NS111678: “Neurofilament light (NfL) chain as a biomarker…
- W81XWH1910622: “Targeting Neurotoxic Glia to Promote Myelin…
- NMSS 1907-34756: “Mechanisms of complement component 3 media…
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